Age differences in the variability and distribution of sleep spindle and rapid eye movement densities

Spindle-slow wave coupling and problem-solving skills: impact of age

We examined how aging affects the role of sleep in the consolidation of newly learned cognitive strategies. Forty healthy young adults (20-35 years) and 30 healthy older adults (60-85 years) were included. Participants were trained on the Tower of Hanoi (ToH) task, then, half of each age group were assigned to either the 90-minute nap condition, or stayed awake, before retesting. The temporal co-occurrence between slow waves (SW) and sleep spindles (SP) during non-rapid eye movement sleep was examined as a function of age in relation to memory consolidation of problem-solving skills. We found that despite intact learning, older adults derived a reduced benefit of sleep for problem-solving skills relative to younger adults. As expected, the percentage of coupled spindles was lower in older compared to younger individuals from control to testing sessions. Furthermore, coupled spindles in young adults were more strongly coupled to the SW upstate compared to older individuals. Coupled spindles in older individuals were lower in amplitude (mean area under the curve; μV) compared to the young group. Lastly, there was a significant relationship between offline gains in accuracy on the ToH and percent change of spindles coupled to the upstate of the slow wave in older, but not younger adults. Multiple regression revealed that age accounted for differences in offline gains in accuracy, as did spindle coupling during the upstate. These results suggest that with aging, spindle-slow wave coupling decreases. However, the degree of the preservation of coupling with age correlates with the extent of problem-solving skill consolidation during sleep.

A personalized semi-automatic sleep spindle detection (PSASD) framework

BACKGROUND

Sleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone.

NEW METHODS

A Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components.

RESULTS

A single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures.

COMPARISON WITH EXISTING METHODS

PSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches.

CONCLUSIONS

Overall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

Sleep spindles as neurophysiological biomarkers of schizophrenia

Schizophrenia (SCZ) is a complex psychiatric disorder characterized by a wide range of clinical symptoms, including disrupted sleep. In recent years, there has been growing interest in assessing alterations in sleep parameters in patients with SCZ. Sleep spindles are brief (0.5-2 s) bursts of 12- to 16-Hz rhythmic electroencephalogram (EEG) oscillatory activity occurring during non-rapid eye movement (NREM) sleep. Spindles have been implicated in several critical brain functions, including learning, memory and plasticity, and are thought to reflect the integrity of underlying thalamocortical circuits. This review aims to provide an overview of the current research investigating sleep spindles in SCZ. After briefly describing the neurophysiological features of sleep spindles, I will discuss alterations in spindle characteristics observed in SCZ, their associations with the clinical symptomatology of these patients and their putative underlying neuronal and molecular mechanisms. I will then discuss the utility of sleep spindle measures as predictors of treatment response and disease progression. Finally, I will highlight future directions for research in this emerging field, including the prospect of utilizing sleep spindles as neurophysiological biomarkers of SCZ.

The Interactive Role of Sleep and Circadian Rhythms in Episodic Memory in Older Adults

Adequate sleep is essential for healthy physical, emotional, and cognitive functioning, including memory. However, sleep ability worsens with increasing age. Older adults on average have shorter sleep durations and more disrupted sleep compared with younger adults. Age-related sleep changes are thought to contribute to age-related deficits in episodic memory. Nonetheless, the nature of the relationship between sleep and episodic memory deficits in older adults is still unclear. Further complicating this relationship are age-related changes in circadian rhythms such as the shift in chronotype toward morningness and decreased circadian stability, which may influence memory abilities as well. Most sleep and cognitive aging studies do not account for circadian factors, making it unclear whether age-related and sleep-related episodic memory deficits are partly driven by interactions with circadian rhythms. This review will focus on age-related changes in sleep and circadian rhythms and evidence that these factors interact to affect episodic memory, specifically encoding and retrieval. Open questions, methodological considerations, and clinical implications for diagnosis and monitoring of age-related memory impairments are discussed.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening

Introduction

Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease.

Methods

The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance.

Results

The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures.

Discussion

The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

Childhood absence epilepsy patients with cognitive impairment have decreased sleep spindle density

OBJECTIVE

To explore the differences in sleep spindle (SS) characteristics during stage N2 sleep between children with childhood absence epilepsy and healthy controls, and between children with childhood absence epilepsy with or without cognitive impairment.

METHODS

We recruited 29 children (14 females, 15 males, mean age: 8 (2.5) years) with childhood absence epilepsy who did not undergone antiseizure treatments previously and 30 age-matched controls (14 females, 16 males, mean age: 9 (3.0) years). For all patients, data on medical history were collected. Each child was monitored overnight by long-term video electroencephalography and was evaluated by the Wechsler Intelligence Scale for Children-Fourth Edition. Next, we compared anterior SS characteristics, including density, frequency, cycle length, duration, amplitude, and percentage of sleep stages.

RESULTS

The childhood absence epilepsy group exhibited lower spindle density and duration in the first 37.5 min of stage N2 sleep than the control group (P < 0.01). A decrease in spindle density could be observed in the childhood absence epilepsy group with aggravated cognition impairment. The spindle density was substantially lower in the cognitively impaired group than in the cognitively unimpaired group (P < 0.01). No significant differences were observed in SS amplitude, SS frequency, SS cycle length, and the distribution of sleep stages.

CONCLUSIONS

Reduction in spindle density and duration is associated with the mechanisms underlying childhood absence epilepsy. The deficit in SS density is related with impaired cognition. This deficiency in SSs may be a useful predictive indicator of cognitive impairment in children with absence epilepsy, indicating that SSs may become a useful biomarker and potential adjuvant anti-seizure target for cognitive impairment caused by childhood absence epilepsy.

Overnight Distribution of REM Sleep Features in People with Parkinson's Disease (PD) and Non-PD Controls

BACKGROUND

Rapid eye movement (REM) sleep behavior disorder (RBD) is a leading predictor of Parkinson's disease (PD). Diagnosis is performed in the sleep laboratory by detecting pathological REM sleep without atonia (RSWA). The evidence on the overnight distribution of RSWA% is conflicting.

OBJECTIVE

To investigate the temporal distribution of the number of ocular movements per REM sleep minute (REM density), and RSWA% in people with PD and non-PD controls.

METHODS

All participants underwent a single overnight evaluation in a sleep laboratory. Clinical evaluation was performed on a separate day. REM density and RSWA% were compared between PD and controls both across four sleep periods and individual REM cycles.

RESULTS

A total of 51 participants with recorded RSWA in polysomnography laboratory were included, 28 with PD aged 64±9 years with a disease duration of 3.3±2.9 years, and 23 controls aged 55±8 years. People with PD had lower REM density and higher RSWA% compared to controls. As expected, REM density was higher towards the morning. In contrast, RSWA% was equally distributed across the night, for both PD and controls.

CONCLUSIONS

PD pathology affects REM sleep features, but not the overnight distribution of those features. While REM density increased towards the end of the night, RSWA% was equally distributed across the night for both PD and controls. Our findings have clinical implications for diagnosing RBD, as quantification of RSWA% in any sleep cycle is sufficient for reliably evaluating total RSWA% and reduced REM density may be a marker of PD.

The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method for fundamental neuroscience and perhaps for clinical applications such as restoring degraded memory function; however, existing tools are expensive, cumbersome, and offer limited experimental flexibility. In this article, we propose the Portiloop, a deep learning-based, portable and low-cost closed-loop stimulation system able to target specific brain oscillations. We first document open-hardware implementations that can be constructed from commercially available components. We also provide a fast, lightweight neural network model and an exploration algorithm that automatically optimizes the model hyperparameters to the desired brain oscillation. Finally, we validate the technology on a challenging test case of real-time sleep spindle detection, with results comparable to off-line expert performance on the Massive Online Data Annotation spindle dataset (MODA; group consensus). Software and plans are available to the community as an open science initiative to encourage further development and advance closed-loop neuroscience research [https://github.com/Portiloop].

Aging-Related Changes in Cortical Sources of Sleep Oscillatory Neural Activity Following Motor Learning Reflect Contributions of Cortical Thickness and Pre-sleep Functional Activity

Oscillatory neural activity during sleep, such as that in the delta and sigma bands, is important for motor learning consolidation. This activity is reduced with typical aging, and this reduction may contribute to aging-related declines in motor learning consolidation. Evidence suggests that brain regions involved in motor learning contribute to oscillatory neural activity during subsequent sleep. However, aging-related differences in regional contributions to sleep oscillatory activity following motor learning are unclear. To characterize these differences, we estimated the cortical sources of consolidation-related oscillatory activity using individual anatomical information in young and older adults during non-rapid eye movement sleep after motor learning and analyzed them in light of cortical thickness and pre-sleep functional brain activation. High-density electroencephalogram was recorded from young and older adults during a midday nap, following completion of a functional magnetic resonance imaged serial reaction time task as part of a larger experimental protocol. Sleep delta activity was reduced with age in a left-weighted motor cortical network, including premotor cortex, primary motor cortex, supplementary motor area, and pre-supplementary motor area, as well as non-motor regions in parietal, temporal, occipital, and cingulate cortices. Sleep theta activity was reduced with age in a similar left-weighted motor network, and in non-motor prefrontal and middle cingulate regions. Sleep sigma activity was reduced with age in left primary motor cortex, in a non-motor right-weighted prefrontal-temporal network, and in cingulate regions. Cortical thinning mediated aging-related sigma reductions in lateral orbitofrontal cortex and frontal pole, and partially mediated delta reductions in parahippocampal, fusiform, and lingual gyri. Putamen, caudate, and inferior parietal cortex activation prior to sleep predicted frontal and motor cortical contributions to sleep delta and theta activity in an age-moderated fashion, reflecting negative relationships in young adults and positive or absent relationships in older adults. Overall, these results support the local sleep hypothesis that brain regions active during learning contribute to consolidation-related neural activity during subsequent sleep and demonstrate that sleep oscillatory activity in these regions is reduced with aging.

Ageing-related changes in nap neuroscillatory activity are mediated and moderated by grey matter volume

Ageing‐related changes in grey matter result in changes in the intensity and topography of sleep neural activity. However, it is unclear whether these findings can be explained by ageing‐related differences in sleep pressure or circadian influence. The current study used high‐density electroencephalography to assess how grey matter volume differences between young and older adults mediate and moderate neuroscillatory activity differences during a midday nap following a motor sequencing task. Delta, theta, and sigma amplitude were reduced in older relative to young adults, especially over frontocentral scalp, leading to increases in relative delta frontality and relative sigma lateral centroposteriority. Delta reductions in older adults were mediated by grey matter loss in frontal medial cortex, primary motor cortex, thalamus, caudate, putamen, and pallidum, and were moderated by putamen grey matter volume. Theta reductions were mediated by grey matter loss in primary motor cortex, thalamus, and caudate, and were moderated by putamen and pallidum grey matter volume. Sigma changes were moderated by putamen and pallidum grey matter volume. Moderation results suggested that across frequencies, young adults with more grey matter had increased activity, whereas older adults with more grey matter had unchanged or decreased activity. These results provide a critical extension of previous findings from overnight sleep in a midday nap, indicating that they are not driven by sleep pressure or circadian confounds. Moreover, these results suggest brain regions associated with motor sequence learning contribute to sleep neural activity following a motor sequencing task.

Neural Correlates of Motor Skill Learning Are Dependent on Both Age and Task Difficulty

Although a general age-related decline in neural plasticity is evident, the effects of age on neural plasticity after motor practice are inconclusive. Inconsistencies in the literature may be related to between-study differences in task difficulty. Therefore, we aimed to determine the effects of age and task difficulty on motor learning and associated brain activity. We used task-related electroencephalography (EEG) power in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands to assess neural plasticity before, immediately after, and 24-h after practice of a mirror star tracing task at one of three difficulty levels in healthy younger (19–24 yr) and older (65–86 yr) adults. Results showed an age-related deterioration in motor performance that was more pronounced with increasing task difficulty and was accompanied by a more bilateral activity pattern for older vs. younger adults. Task difficulty affected motor skill retention and neural plasticity specifically in older adults. Older adults that practiced at the low or medium, but not the high, difficulty levels were able to maintain improvements in accuracy at retention and showed modulation of alpha TR-Power after practice. Together, these data indicate that both age and task difficulty affect motor learning, as well as the associated neural plasticity.

Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data

Spindle event detection is a key component in analyzing human sleep. However, detection of these oscillatory patterns by experts is time consuming and costly. Automated detection algorithms are cost efficient and reproducible but require robust datasets to be trained and validated. Using the MODA (Massive Online Data Annotation) platform, we used crowdsourcing to produce a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects). We evaluated the performance of three subtype scorers: “experts, researchers and non-experts”, as well as 7 previously published spindle detection algorithms. Our findings show that only two algorithms had performance scores similar to human experts. Furthermore, the human scorers agreed on the average spindle characteristics (density, duration and amplitude), but there were significant age and sex differences (also observed in the set of detected spindles). This study demonstrates how the MODA platform can be used to generate a highly valid open source standardized dataset for researchers to train, validate and compare automated detectors of biological signals such as the EEG.

Sleep spindle detection using deep learning: A validation study based on crowdsourcing

Sleep spindles are significant transient oscillations observed on the electroencephalogram (EEG) in stage 2 of non-rapid eye movement sleep. Deep belief network (DBN) gaining great successes in images and speech is still a novel method to develop sleep spindle detection system. In this paper, crowdsourcing replacing gold standard was applied to generate three different labeled samples and constructed three classes of datasets with a combination of these samples. An F1-score measure was estimated to compare the performance of DBN to other three classifiers on classifying these samples, with the DBN obtaining an result of 92.78%. Then a comparison of two feature extraction methods based on power spectrum density was made on same dataset using DBN. In addition, the DBN trained in dataset was applied to detect sleep spindle from raw EEG recordings and performed a comparable capacity to expert group consensus.

Physiological and pathological high-frequency oscillations have distinct sleep-homeostatic properties

OBJECTIVE

The stage of sleep is a known modulator of high-frequency oscillations (HFOs). For instance, high amplitude slow waves during NREM sleep and the subtypes of REM sleep were shown to contribute to a better separation between physiological and pathological HFOs. This study investigated rates and spatial spread of the different HFO types (physiological and pathological ripples in the 80-250 Hz frequency band, and fast ripples above 250 Hz) depending on time spent in sleep across the different sleep cycles.

METHODS

Fifteen patients with focal pharmaco-resistant epilepsy underwent one night of video-polysomnography during chronic intracranial EEG recording for presurgical epilepsy evaluation. The HFO rate and spread across the different sleep cycles were determined with an automatic HFO detector. We built models to explain the observed rate and spread based on time in sleep and other variables i.e. sleep stage, delta band and sigma band activity, and slow wave amplitude. Statistical significance of the different variables was determined by a model comparison using the Akaike information criterion.

RESULTS

The rate of HFOs depends significantly on the accumulated time of sleep. As the night advanced, the rate of pathological ripples and fast ripples decreased during NREM sleep (up to 15% per hour spent in the respective sleep stages), while the rate of physiological ripples increased during REM sleep (8% per hour spent in REM sleep). Interestingly, the stage of sleep but not the sleep cycle determined the extent of spread of HFOs, showing a larger field during NREM sleep and a more restricted field during REM sleep.

CONCLUSION

The different dependence with sleep time for physiological and pathological ripples is in keeping with their distinct underlying generating mechanisms. From a practical point of view, the first sleep cycle seems to be best suitable for studying HFOs in epilepsy, given that the contrast between physiological and pathological ripple rates is largest during this time.

Sleep spindles: a physiological marker of age-related changes in gray matter in brain regions supporting motor skill memory consolidation

Sleep is necessary for the optimal consolidation of procedural learning, and in particular, for motor sequential skills. Motor sequence learning remains intact with age, but sleep-dependent consolidation is impaired, suggesting that memory deficits for procedural skills are specifically impacted by age-related changes in sleep. Age-related changes in spindles may be responsible for impaired motor sequence learning consolidation, but the morphological basis for this deficit is unknown. Here, we found that gray matter in the hippocampus and cerebellum was positively correlated with both sleep spindles and offline improvements in performance in young participants but not in older participants. These results suggest that age-related changes in gray matter in the hippocampus relate to spindles and may underlie age-related deficits in sleep-related motor sequence memory consolidation. In this way, spindles can serve as a biological marker for structural brain changes and the related memory deficits in older adults.

EEG desynchronization during phasic REM sleep suppresses interictal epileptic activity in humans

Objective

Rapid eye movement (REM) sleep has a suppressing effect on epileptic activity. This effect might be directly related to neuronal desynchronization mediated by cholinergic neurotransmission. We investigated whether interictal epileptiform discharges (IEDs) and high frequency oscillations—a biomarker of the epileptogenic zone—are evenly distributed across phasic and tonic REM sleep. We hypothesized that IEDs are more suppressed during phasic REM sleep because of additional cholinergic drive.

Methods

Twelve patients underwent polysomnography during long‐term combined scalp‐intracerebral electroencephalography (EEG) recording. After sleep staging in the scalp EEG, we identified segments of REM sleep with rapid eye movements (phasic REM) and segments of REM sleep without rapid eye movements (tonic REM). In the intracerebral EEG, we computed the power in frequencies <30 Hz and from 30 to 500 Hz, and marked IEDs, ripples (>80 Hz) and fast ripples (>250 Hz). We grouped the intracerebral channels into channels in the seizure‐onset zone (SOZ), the exclusively irritative zone (EIZ), and the normal zone (NoZ).

Results

Power in frequencies <30 Hz was lower during phasic than tonic REM sleep (p < 0.001), most likely reflecting increased desynchronization. IEDs, ripples and fast ripples, were less frequent during phasic than tonic REM sleep (phasic REM sleep: 39% of spikes, 35% of ripples, 18% of fast ripples, tonic REM sleep: 61% of spikes, 65% of ripples, 82% of fast ripples; p < 0.001). In contrast to ripples in the epileptogenic zone, physiologic ripples were more abundant during phasic REM sleep (phasic REM sleep: 73% in NoZ, 30% in EIZ, 28% in SOZ, tonic REM sleep: 27% in NoZ, 70% in EIZ, 72% in SOZ; p < 0.001).

Significance

Phasic REM sleep has an enhanced suppressive effect on IEDs, corroborating the role of EEG desynchronization in the suppression of interictal epileptic activity. In contrast, physiologic ripples were increased during phasic REM sleep, possibly reflecting REM‐related memory consolidation and dreaming.

Form and Function of Sleep Spindles across the Lifespan

Since the advent of EEG recordings, sleep spindles have been identified as hallmarks of non-REM sleep. Despite a broad general understanding of mechanisms of spindle generation gleaned from animal studies, the mechanisms underlying certain features of spindles in the human brain, such as “global” versus “local” spindles, are largely unknown. Neither the topography nor the morphology of sleep spindles remains constant throughout the lifespan. It is likely that changes in spindle phenomenology during development and aging are the result of dramatic changes in brain structure and function. Across various developmental windows, spindle activity is correlated with general cognitive aptitude, learning, and memory; however, these correlations vary in strength, and even direction, depending on age and metrics used. Understanding these differences across the lifespan should further clarify how these oscillations are generated and their function under a variety of circumstances. We discuss these issues, and their translational implications for human cognitive function. Because sleep spindles are similarly affected in disorders of neurodevelopment (such as schizophrenia) and during aging (such as neurodegenerative conditions), both types of disorders may benefit from therapies based on a better understanding of spindle function.

Spindle Oscillations in Sleep Disorders: A Systematic Review

Measurement of sleep microarchitecture and neural oscillations is an increasingly popular technique for quantifying EEG sleep activity. Many studies have examined sleep spindle oscillations in sleep-disordered adults; however reviews of this literature are scarce. As such, our overarching aim was to critically review experimental studies examining sleep spindle activity between adults with and without different sleep disorders. Articles were obtained using a systematic methodology with a priori criteria. Thirty-seven studies meeting final inclusion criteria were reviewed, with studies grouped across three categories: insomnia, hypersomnias, and sleep-related movement disorders (including parasomnias). Studies of patients with insomnia and sleep-disordered breathing were more abundant relative to other diagnoses. All studies were cross-sectional. Studies were largely inconsistent regarding spindle activity differences between clinical and nonclinical groups, with some reporting greater or less activity, while many others reported no group differences. Stark inconsistencies in sample characteristics (e.g., age range and diagnostic criteria) and methods of analysis (e.g., spindle bandwidth selection, visual detection versus digital filtering, absolute versus relative spectral power, and NREM2 versus NREM3) suggest a need for greater use of event-based detection methods and increased research standardization. Hypotheses regarding the clinical and empirical implications of these findings, and suggestions for potential future studies, are also discussed.

The 5α-reductase inhibitor finasteride is not associated with alterations in sleep spindles in men referred for polysomnography

OBJECTIVE

Endogenous neurosteroids that potentiate the gamma-aminobutyric acid type A (GABAA ) receptor are thought to enhance the generation of sleep spindles. This study tested the hypothesis that the 5α-reductase inhibitor finasteride, an agent associated with reductions in neurosteroids, would be associated with reduced sleep spindles in men referred for polysomnography.

METHODS

Spectral analysis and spindle waveform detection were performed on electroencephalographic (EEG) sleep data in the 11-16 Hz sigma band, as well as several subranges, from 27 men taking finasteride and 27 matched comparison patients (ages 18 to 81 years).

RESULTS

No significant differences between groups were observed for spectral power or sleep spindle morphology measures, including spindle density, amplitude, duration, and integrated spindle activity.

CONCLUSIONS

Contrary to our hypothesis, these findings demonstrate that finasteride is not associated with alterations in sleep spindle range activity or spindle morphology parameters.